@inproceedings{vaidyanathan-2025-hybrid,
    title = "A Hybrid Retrieval System for Adverse Event Concept Normalization Integrating Contextual Scoring, Lexical Augmentation, and Semantic Fine-Tuning",
    author = "Vaidyanathan, Saipriya Dipika",
    editor = "Kummerfeld, Jonathan K.  and
      Joshi, Aditya  and
      Dras, Mark",
    booktitle = "Proceedings of The 23rd Annual Workshop of the Australasian Language Technology Association",
    month = nov,
    year = "2025",
    address = "Sydney, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-alta/2025.alta-main.19/",
    pages = "240--244",
    ISBN = "1834-7037",
    abstract = "This paper presents a fully automated pipeline for normalizing adverse drug event (ADE) mentions identified in user-generated medical texts, to MedDRA concepts. The core approach here is a hybrid retrieval architecture combining domain-specific phrase normalization, synonym augmentation, and explicit mappings for key symptoms, thereby improving coverage of lexical variants. For candidate generation, the system employs a blend of exact dictionary lookups and fuzzy matching, supplemented by drug-specific contextual scoring. A sentencetransformer model (distilroberta-v1) was finetuned on augmented phrases, with reciprocal rank fusion unifying multiple retrieval signals."
}